Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A General Evaluation Framework for Topical Crawlers
Information Retrieval
Learning to crawl: Comparing classification schemes
ACM Transactions on Information Systems (TOIS)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A conceptual framework for efficient web crawling in virtual integration contexts
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
FDIA'09 Proceedings of the Third BCS-IRSG conference on Future Directions in Information Access
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Focused crawlers are programs that wander in the Web, using its graph structure, and gather pages that belong to a specific topic. The most critical task in Focused Crawling is the scoring of the URLs as it designates the path that the crawler will follow, and thus its effectiveness. In this paper we propose a novel scheme for assigning scores to the URLs, based on the Reinforcement Learning (RL) framework. The proposed approach learns to select the best classifier for ordering the URLs. This formulation reduces the size of the search space for the RL method and makes the problem tractable. We evaluate the proposed approach on-line on a number of topics, which offers a realistic view of its performance, comparing it also with a RL method and a simple but effective classifier-based crawler. The results demonstrate the strength of the proposed approach.